Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations2266
Missing cells11565
Missing cells (%)19.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory857.0 B

Variable types

Text2
DateTime1
Categorical12
Numeric11

Alerts

cant_apercibimientos has constant value "1.0" Constant
cant_suspensiones has constant value "2.0" Constant
Cluster_6 has constant value "3" Constant
Estado is highly overall correlated with cant_antecedentesHigh correlation
TipoSocietario is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
anio_preinscripcion is highly overall correlated with antiguedad and 2 other fieldsHigh correlation
antiguedad is highly overall correlated with anio_preinscripcion and 2 other fieldsHigh correlation
cant_Apoderado is highly overall correlated with cant_antecedentes and 2 other fieldsHigh correlation
cant_MontoLimite is highly overall correlated with TipoSocietario and 6 other fieldsHigh correlation
cant_antecedentes is highly overall correlated with Estado and 16 other fieldsHigh correlation
cant_autenticado is highly overall correlated with cant_antecedentesHigh correlation
cant_noAutenticado is highly overall correlated with cant_Apoderado and 3 other fieldsHigh correlation
cant_procesos_adjudicado is highly overall correlated with cant_antecedentes and 1 other fieldsHigh correlation
cant_representante is highly overall correlated with cant_MontoLimiteHigh correlation
cant_sinMontoLimite is highly overall correlated with cant_Apoderado and 2 other fieldsHigh correlation
cant_socios is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
dcant_procesos_adjudicado is highly overall correlated with cant_antecedentesHigh correlation
dmonto_total_adjudicado is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
dtotal_articulos_provee is highly overall correlated with cant_antecedentes and 1 other fieldsHigh correlation
monto_total_adjudicado is highly overall correlated with cant_MontoLimite and 2 other fieldsHigh correlation
periodo_preinscripcion is highly overall correlated with anio_preinscripcion and 2 other fieldsHigh correlation
provincia is highly overall correlated with cant_antecedentesHigh correlation
total_articulos_provee is highly overall correlated with cant_MontoLimite and 2 other fieldsHigh correlation
Estado is highly imbalanced (61.8%) Imbalance
cant_socios has 280 (12.4%) missing values Missing
cant_apercibimientos has 2265 (> 99.9%) missing values Missing
cant_suspensiones has 2265 (> 99.9%) missing values Missing
cant_antecedentes has 2264 (99.9%) missing values Missing
cant_Apoderado has 233 (10.3%) missing values Missing
cant_representante has 725 (32.0%) missing values Missing
cant_noAutenticado has 1222 (53.9%) missing values Missing
cant_MontoLimite has 2261 (99.8%) missing values Missing
cant_antecedentes is uniformly distributed Uniform
CUIT has unique values Unique
antiguedad has 198 (8.7%) zeros Zeros

Reproduction

Analysis started2025-06-30 18:09:48.877350
Analysis finished2025-06-30 18:10:02.376952
Duration13.5 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct2266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size168.1 KiB
2025-06-30T15:10:02.501582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length11
Mean length10.978376
Min length8

Characters and Unicode

Total characters24877
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2266 ?
Unique (%)100.0%

Sample

1st row20305924076
2nd row30710210000
3rd row33714924619
4th row33561600959
5th row30694465591
ValueCountFrequency (%)
30568246582 1
 
< 0.1%
30518773743 1
 
< 0.1%
20305924076 1
 
< 0.1%
30710210000 1
 
< 0.1%
33714924619 1
 
< 0.1%
33561600959 1
 
< 0.1%
30694465591 1
 
< 0.1%
30707870571 1
 
< 0.1%
30708995157 1
 
< 0.1%
30500106316 1
 
< 0.1%
Other values (2256) 2256
99.6%
2025-06-30T15:10:02.693983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4311
17.3%
3 3733
15.0%
7 2924
11.8%
1 2556
10.3%
6 2146
8.6%
5 1981
8.0%
2 1889
7.6%
9 1880
7.6%
8 1719
 
6.9%
4 1677
 
6.7%
Other values (21) 61
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24877
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4311
17.3%
3 3733
15.0%
7 2924
11.8%
1 2556
10.3%
6 2146
8.6%
5 1981
8.0%
2 1889
7.6%
9 1880
7.6%
8 1719
 
6.9%
4 1677
 
6.7%
Other values (21) 61
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24877
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4311
17.3%
3 3733
15.0%
7 2924
11.8%
1 2556
10.3%
6 2146
8.6%
5 1981
8.0%
2 1889
7.6%
9 1880
7.6%
8 1719
 
6.9%
4 1677
 
6.7%
Other values (21) 61
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24877
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4311
17.3%
3 3733
15.0%
7 2924
11.8%
1 2556
10.3%
6 2146
8.6%
5 1981
8.0%
2 1889
7.6%
9 1880
7.6%
8 1719
 
6.9%
4 1677
 
6.7%
Other values (21) 61
 
0.2%

Nombre
Text

Distinct2223
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size198.2 KiB
2025-06-30T15:10:02.877772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length121
Median length70
Mean length21.074581
Min length2

Characters and Unicode

Total characters47755
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2221 ?
Unique (%)98.0%

Sample

1st rowSuministros EDA
2nd rowTECNARAN SRL
3rd rowSIGNIFY ARGENTINA S.A.
4th rowRognoni y CIA SA
5th rowADSUR S.A..
ValueCountFrequency (%)
s.a 650
 
8.8%
srl 417
 
5.6%
sa 305
 
4.1%
de 266
 
3.6%
s.r.l 227
 
3.1%
argentina 143
 
1.9%
y 140
 
1.9%
la 56
 
0.8%
servicios 54
 
0.7%
sin 43
 
0.6%
Other values (3162) 5098
68.9%
2025-06-30T15:10:03.494491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5133
 
10.7%
A 4095
 
8.6%
S 3423
 
7.2%
R 2554
 
5.3%
E 2484
 
5.2%
I 2364
 
5.0%
. 2238
 
4.7%
O 1989
 
4.2%
L 1873
 
3.9%
N 1735
 
3.6%
Other values (81) 19867
41.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47755
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5133
 
10.7%
A 4095
 
8.6%
S 3423
 
7.2%
R 2554
 
5.3%
E 2484
 
5.2%
I 2364
 
5.0%
. 2238
 
4.7%
O 1989
 
4.2%
L 1873
 
3.9%
N 1735
 
3.6%
Other values (81) 19867
41.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47755
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5133
 
10.7%
A 4095
 
8.6%
S 3423
 
7.2%
R 2554
 
5.3%
E 2484
 
5.2%
I 2364
 
5.0%
. 2238
 
4.7%
O 1989
 
4.2%
L 1873
 
3.9%
N 1735
 
3.6%
Other values (81) 19867
41.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47755
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5133
 
10.7%
A 4095
 
8.6%
S 3423
 
7.2%
R 2554
 
5.3%
E 2484
 
5.2%
I 2364
 
5.0%
. 2238
 
4.7%
O 1989
 
4.2%
L 1873
 
3.9%
N 1735
 
3.6%
Other values (81) 19867
41.6%
Distinct972
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size35.4 KiB
Minimum2016-01-08 00:00:00
Maximum2022-12-09 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-30T15:10:03.611220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:03.744653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estado
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size176.6 KiB
Inscripto
1778 
Desactualizado doc. vencidos
 
155
Pre Inscripto
 
138
Desactualizado mantención
 
135
Desactualizado Por Clase
 
31
Other values (4)
 
29

Length

Max length28
Median length9
Mean length11.786849
Min length9

Characters and Unicode

Total characters26709
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowInscripto
2nd rowInscripto
3rd rowInscripto
4th rowInscripto
5th rowInscripto

Common Values

ValueCountFrequency (%)
Inscripto 1778
78.5%
Desactualizado doc. vencidos 155
 
6.8%
Pre Inscripto 138
 
6.1%
Desactualizado mantención 135
 
6.0%
Desactualizado Por Clase 31
 
1.4%
En Evaluacion 17
 
0.8%
Con Solicitud De Baja 10
 
0.4%
Inhabilitado 1
 
< 0.1%
Dar De Baja 1
 
< 0.1%

Length

2025-06-30T15:10:03.862994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:03.942519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 1916
64.7%
desactualizado 321
 
10.8%
doc 155
 
5.2%
vencidos 155
 
5.2%
pre 138
 
4.7%
mantención 135
 
4.6%
por 31
 
1.0%
clase 31
 
1.0%
en 17
 
0.6%
evaluacion 17
 
0.6%
Other values (6) 44
 
1.5%

Most occurring characters

ValueCountFrequency (%)
c 2709
10.1%
o 2616
9.8%
i 2566
9.6%
n 2521
9.4%
s 2423
9.1%
t 2383
8.9%
r 2086
7.8%
I 1917
7.2%
p 1916
7.2%
a 1188
 
4.4%
Other values (19) 4384
16.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26709
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 2709
10.1%
o 2616
9.8%
i 2566
9.6%
n 2521
9.4%
s 2423
9.1%
t 2383
8.9%
r 2086
7.8%
I 1917
7.2%
p 1916
7.2%
a 1188
 
4.4%
Other values (19) 4384
16.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26709
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 2709
10.1%
o 2616
9.8%
i 2566
9.6%
n 2521
9.4%
s 2423
9.1%
t 2383
8.9%
r 2086
7.8%
I 1917
7.2%
p 1916
7.2%
a 1188
 
4.4%
Other values (19) 4384
16.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26709
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 2709
10.1%
o 2616
9.8%
i 2566
9.6%
n 2521
9.4%
s 2423
9.1%
t 2383
8.9%
r 2086
7.8%
I 1917
7.2%
p 1916
7.2%
a 1188
 
4.4%
Other values (19) 4384
16.4%

TipoSocietario
Categorical

High correlation 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size224.2 KiB
Sociedad Anónima
1054 
S.R.L
673 
Persona Física
195 
Otras Formas Societarias
 
92
Organismo Publico
 
84
Other values (6)
168 

Length

Max length29
Median length26
Mean length13.260371
Min length5

Characters and Unicode

Total characters30048
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowPersona Física
2nd rowS.R.L
3rd rowSociedad Anónima
4th rowSociedad Anónima
5th rowSociedad Anónima

Common Values

ValueCountFrequency (%)
Sociedad Anónima 1054
46.5%
S.R.L 673
29.7%
Persona Física 195
 
8.6%
Otras Formas Societarias 92
 
4.1%
Organismo Publico 84
 
3.7%
PJ Extranjero Sin Sucursal 79
 
3.5%
Cooperativas 47
 
2.1%
Sociedades De Hecho 37
 
1.6%
Unión Transitoria de Empresas 3
 
0.1%
PF Extranjero No Residente 1
 
< 0.1%

Length

2025-06-30T15:10:04.060430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sociedad 1054
25.7%
anónima 1054
25.7%
s.r.l 673
16.4%
persona 195
 
4.7%
física 195
 
4.7%
otras 92
 
2.2%
formas 92
 
2.2%
societarias 92
 
2.2%
organismo 84
 
2.0%
publico 84
 
2.0%
Other values (16) 492
12.0%

Most occurring characters

ValueCountFrequency (%)
a 3250
 
10.8%
i 2829
 
9.4%
n 2556
 
8.5%
d 2187
 
7.3%
S 2014
 
6.7%
o 1855
 
6.2%
1841
 
6.1%
e 1628
 
5.4%
c 1578
 
5.3%
. 1346
 
4.5%
Other values (29) 8964
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30048
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3250
 
10.8%
i 2829
 
9.4%
n 2556
 
8.5%
d 2187
 
7.3%
S 2014
 
6.7%
o 1855
 
6.2%
1841
 
6.1%
e 1628
 
5.4%
c 1578
 
5.3%
. 1346
 
4.5%
Other values (29) 8964
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30048
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3250
 
10.8%
i 2829
 
9.4%
n 2556
 
8.5%
d 2187
 
7.3%
S 2014
 
6.7%
o 1855
 
6.2%
1841
 
6.1%
e 1628
 
5.4%
c 1578
 
5.3%
. 1346
 
4.5%
Other values (29) 8964
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30048
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3250
 
10.8%
i 2829
 
9.4%
n 2556
 
8.5%
d 2187
 
7.3%
S 2014
 
6.7%
o 1855
 
6.2%
1841
 
6.1%
e 1628
 
5.4%
c 1578
 
5.3%
. 1346
 
4.5%
Other values (29) 8964
29.8%

periodo_preinscripcion
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201781.89
Minimum201607
Maximum202211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.4 KiB
2025-06-30T15:10:04.278535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum201607
5-th percentile201610
Q1201702
median201709
Q3201811
95-th percentile202107
Maximum202211
Range604
Interquartile range (IQR)109

Descriptive statistics

Standard deviation155.97205
Coefficient of variation (CV)0.00077297347
Kurtosis0.35256471
Mean201781.89
Median Absolute Deviation (MAD)97
Skewness1.1029482
Sum4.5723777 × 108
Variance24327.281
MonotonicityNot monotonic
2025-06-30T15:10:04.381095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201611 161
 
7.1%
201701 109
 
4.8%
201705 106
 
4.7%
201706 100
 
4.4%
201704 98
 
4.3%
201703 94
 
4.1%
201702 83
 
3.7%
201612 82
 
3.6%
201610 78
 
3.4%
201708 75
 
3.3%
Other values (67) 1280
56.5%
ValueCountFrequency (%)
201607 4
 
0.2%
201608 35
 
1.5%
201609 45
 
2.0%
201610 78
3.4%
201611 161
7.1%
201612 82
3.6%
201701 109
4.8%
201702 83
3.7%
201703 94
4.1%
201704 98
4.3%
ValueCountFrequency (%)
202211 5
0.2%
202210 6
0.3%
202209 6
0.3%
202208 4
 
0.2%
202207 3
 
0.1%
202206 6
0.3%
202205 9
0.4%
202204 11
0.5%
202203 7
0.3%
202202 8
0.4%

anio_preinscripcion
Categorical

High correlation 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size152.7 KiB
2017
943 
2016
405 
2018
370 
2020
181 
2019
169 
Other values (2)
198 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters9064
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2017 943
41.6%
2016 405
17.9%
2018 370
 
16.3%
2020 181
 
8.0%
2019 169
 
7.5%
2021 131
 
5.8%
2022 67
 
3.0%

Length

2025-06-30T15:10:04.474826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:04.537311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2017 943
41.6%
2016 405
17.9%
2018 370
 
16.3%
2020 181
 
8.0%
2019 169
 
7.5%
2021 131
 
5.8%
2022 67
 
3.0%

Most occurring characters

ValueCountFrequency (%)
2 2712
29.9%
0 2447
27.0%
1 2018
22.3%
7 943
 
10.4%
6 405
 
4.5%
8 370
 
4.1%
9 169
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2712
29.9%
0 2447
27.0%
1 2018
22.3%
7 943
 
10.4%
6 405
 
4.5%
8 370
 
4.1%
9 169
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2712
29.9%
0 2447
27.0%
1 2018
22.3%
7 943
 
10.4%
6 405
 
4.5%
8 370
 
4.1%
9 169
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2712
29.9%
0 2447
27.0%
1 2018
22.3%
7 943
 
10.4%
6 405
 
4.5%
8 370
 
4.1%
9 169
 
1.9%

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)4.2%
Missing16
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean8.4675556
Minimum1
Maximum189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.4 KiB
2025-06-30T15:10:04.631875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q38
95-th percentile37
Maximum189
Range188
Interquartile range (IQR)7

Descriptive statistics

Standard deviation16.992821
Coefficient of variation (CV)2.0068154
Kurtosis33.421103
Mean8.4675556
Median Absolute Deviation (MAD)2
Skewness5.0171123
Sum19052
Variance288.75595
MonotonicityNot monotonic
2025-06-30T15:10:04.763866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 726
32.0%
2 333
14.7%
3 231
 
10.2%
4 148
 
6.5%
6 93
 
4.1%
5 91
 
4.0%
7 61
 
2.7%
9 47
 
2.1%
8 45
 
2.0%
11 43
 
1.9%
Other values (85) 432
19.1%
ValueCountFrequency (%)
1 726
32.0%
2 333
14.7%
3 231
 
10.2%
4 148
 
6.5%
5 91
 
4.0%
6 93
 
4.1%
7 61
 
2.7%
8 45
 
2.0%
9 47
 
2.1%
10 41
 
1.8%
ValueCountFrequency (%)
189 1
< 0.1%
178 1
< 0.1%
164 1
< 0.1%
154 1
< 0.1%
153 2
0.1%
150 1
< 0.1%
147 1
< 0.1%
141 1
< 0.1%
136 1
< 0.1%
131 1
< 0.1%

monto_total_adjudicado
Real number (ℝ)

High correlation 

Distinct2231
Distinct (%)99.2%
Missing16
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean74485595
Minimum0
Maximum4.7156253 × 109
Zeros15
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size35.4 KiB
2025-06-30T15:10:04.894599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45344.059
Q11147825.6
median7699800
Q338481576
95-th percentile3.5726319 × 108
Maximum4.7156253 × 109
Range4.7156253 × 109
Interquartile range (IQR)37333751

Descriptive statistics

Standard deviation2.6464261 × 108
Coefficient of variation (CV)3.5529367
Kurtosis125.0544
Mean74485595
Median Absolute Deviation (MAD)7467476.5
Skewness9.5881652
Sum1.6759259 × 1011
Variance7.0035708 × 1016
MonotonicityNot monotonic
2025-06-30T15:10:05.010992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
0.7%
1419000 5
 
0.2%
510000 2
 
0.1%
779521.8419 1
 
< 0.1%
162919683.1 1
 
< 0.1%
55590000 1
 
< 0.1%
6523337.4 1
 
< 0.1%
1237315257 1
 
< 0.1%
1354243492 1
 
< 0.1%
1624050032 1
 
< 0.1%
Other values (2221) 2221
98.0%
(Missing) 16
 
0.7%
ValueCountFrequency (%)
0 15
0.7%
1.7 1
 
< 0.1%
36.13714286 1
 
< 0.1%
516.24 1
 
< 0.1%
805.37 1
 
< 0.1%
1190 1
 
< 0.1%
2240 1
 
< 0.1%
2412.02 1
 
< 0.1%
2450 1
 
< 0.1%
2662 1
 
< 0.1%
ValueCountFrequency (%)
4715625322 1
< 0.1%
4065619873 1
< 0.1%
3933614343 1
< 0.1%
3921473681 1
< 0.1%
3226132956 1
< 0.1%
2721495852 1
< 0.1%
2413701716 1
< 0.1%
2026657207 1
< 0.1%
1873442435 1
< 0.1%
1624050032 1
< 0.1%

antiguedad
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2771403
Minimum0
Maximum5
Zeros198
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size35.4 KiB
2025-06-30T15:10:05.102324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4952398
Coefficient of variation (CV)0.45626359
Kurtosis-0.17168088
Mean3.2771403
Median Absolute Deviation (MAD)1
Skewness-0.93038713
Sum7426
Variance2.2357421
MonotonicityNot monotonic
2025-06-30T15:10:05.178053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 943
41.6%
5 405
17.9%
3 370
 
16.3%
0 198
 
8.7%
1 181
 
8.0%
2 169
 
7.5%
ValueCountFrequency (%)
0 198
 
8.7%
1 181
 
8.0%
2 169
 
7.5%
3 370
 
16.3%
4 943
41.6%
5 405
17.9%
ValueCountFrequency (%)
5 405
17.9%
4 943
41.6%
3 370
 
16.3%
2 169
 
7.5%
1 181
 
8.0%
0 198
 
8.7%

provincia
Categorical

High correlation 

Distinct27
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size165.0 KiB
CABA
1108 
Buenos Aires
417 
Córdoba
133 
Santa Fe
 
100
Extranjera
 
80
Other values (22)
428 

Length

Max length19
Median length16
Mean length6.8909974
Min length4

Characters and Unicode

Total characters15615
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCABA
2nd rowCABA
3rd rowBuenos Aires
4th rowBuenos Aires
5th rowCABA

Common Values

ValueCountFrequency (%)
CABA 1108
48.9%
Buenos Aires 417
 
18.4%
Córdoba 133
 
5.9%
Santa Fe 100
 
4.4%
Extranjera 80
 
3.5%
Mendoza 69
 
3.0%
Tierra del Fuego 39
 
1.7%
Chubut 32
 
1.4%
Neuquén 30
 
1.3%
Entre Rios 26
 
1.1%
Other values (17) 232
 
10.2%

Length

2025-06-30T15:10:05.268766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
caba 1108
37.0%
buenos 417
 
13.9%
aires 417
 
13.9%
córdoba 133
 
4.4%
santa 116
 
3.9%
fe 100
 
3.3%
extranjera 80
 
2.7%
mendoza 69
 
2.3%
del 48
 
1.6%
tierra 40
 
1.3%
Other values (26) 470
15.7%

Most occurring characters

ValueCountFrequency (%)
A 2633
16.9%
B 1525
9.8%
e 1353
 
8.7%
C 1340
 
8.6%
s 947
 
6.1%
r 922
 
5.9%
n 866
 
5.5%
a 862
 
5.5%
o 818
 
5.2%
732
 
4.7%
Other values (30) 3617
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2633
16.9%
B 1525
9.8%
e 1353
 
8.7%
C 1340
 
8.6%
s 947
 
6.1%
r 922
 
5.9%
n 866
 
5.5%
a 862
 
5.5%
o 818
 
5.2%
732
 
4.7%
Other values (30) 3617
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2633
16.9%
B 1525
9.8%
e 1353
 
8.7%
C 1340
 
8.6%
s 947
 
6.1%
r 922
 
5.9%
n 866
 
5.5%
a 862
 
5.5%
o 818
 
5.2%
732
 
4.7%
Other values (30) 3617
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2633
16.9%
B 1525
9.8%
e 1353
 
8.7%
C 1340
 
8.6%
s 947
 
6.1%
r 922
 
5.9%
n 866
 
5.5%
a 862
 
5.5%
o 818
 
5.2%
732
 
4.7%
Other values (30) 3617
23.2%

cant_socios
Real number (ℝ)

High correlation  Missing 

Distinct12
Distinct (%)0.6%
Missing280
Missing (%)12.4%
Infinite0
Infinite (%)0.0%
Mean2.2089627
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.4 KiB
2025-06-30T15:10:05.354319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2995408
Coefficient of variation (CV)0.58830363
Kurtosis6.872677
Mean2.2089627
Median Absolute Deviation (MAD)1
Skewness1.9010343
Sum4387
Variance1.6888063
MonotonicityNot monotonic
2025-06-30T15:10:05.444251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 769
33.9%
1 640
28.2%
3 311
13.7%
4 144
 
6.4%
5 72
 
3.2%
6 31
 
1.4%
7 10
 
0.4%
8 5
 
0.2%
9 1
 
< 0.1%
10 1
 
< 0.1%
Other values (2) 2
 
0.1%
(Missing) 280
 
12.4%
ValueCountFrequency (%)
1 640
28.2%
2 769
33.9%
3 311
13.7%
4 144
 
6.4%
5 72
 
3.2%
6 31
 
1.4%
7 10
 
0.4%
8 5
 
0.2%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 5
 
0.2%
7 10
 
0.4%
6 31
 
1.4%
5 72
 
3.2%
4 144
6.4%
3 311
13.7%

cant_apercibimientos
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing2265
Missing (%)> 99.9%
Memory size141.6 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 2265
> 99.9%

Length

2025-06-30T15:10:05.527849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:05.576613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

cant_suspensiones
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing2265
Missing (%)> 99.9%
Memory size141.6 KiB
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2.0

Common Values

ValueCountFrequency (%)
2.0 1
 
< 0.1%
(Missing) 2265
> 99.9%

Length

2025-06-30T15:10:05.635178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:05.676713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1
33.3%
. 1
33.3%
0 1
33.3%

cant_antecedentes
Categorical

High correlation  Missing  Uniform 

Distinct2
Distinct (%)100.0%
Missing2264
Missing (%)99.9%
Memory size141.6 KiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row1.0
2nd row2.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
2.0 1
 
< 0.1%
(Missing) 2264
99.9%

Length

2025-06-30T15:10:05.730059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:05.794028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
50.0%
2.0 1
50.0%

Most occurring characters

ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
1 1
16.7%
2 1
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
1 1
16.7%
2 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
1 1
16.7%
2 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
1 1
16.7%
2 1
16.7%

cant_Apoderado
Real number (ℝ)

High correlation  Missing 

Distinct12
Distinct (%)0.6%
Missing233
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean1.7137236
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.4 KiB
2025-06-30T15:10:05.843557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0572647
Coefficient of variation (CV)0.61694004
Kurtosis19.245414
Mean1.7137236
Median Absolute Deviation (MAD)0
Skewness3.3189313
Sum3484
Variance1.1178086
MonotonicityNot monotonic
2025-06-30T15:10:05.918423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1044
46.1%
2 729
32.2%
3 162
 
7.1%
4 57
 
2.5%
5 20
 
0.9%
8 5
 
0.2%
6 5
 
0.2%
7 5
 
0.2%
11 2
 
0.1%
10 2
 
0.1%
Other values (2) 2
 
0.1%
(Missing) 233
 
10.3%
ValueCountFrequency (%)
1 1044
46.1%
2 729
32.2%
3 162
 
7.1%
4 57
 
2.5%
5 20
 
0.9%
6 5
 
0.2%
7 5
 
0.2%
8 5
 
0.2%
9 1
 
< 0.1%
10 2
 
0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
11 2
 
0.1%
10 2
 
0.1%
9 1
 
< 0.1%
8 5
 
0.2%
7 5
 
0.2%
6 5
 
0.2%
5 20
 
0.9%
4 57
 
2.5%
3 162
7.1%

cant_representante
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)0.4%
Missing725
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean1.2712524
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.4 KiB
2025-06-30T15:10:05.973671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.58143155
Coefficient of variation (CV)0.45736908
Kurtosis36.56123
Mean1.2712524
Median Absolute Deviation (MAD)0
Skewness3.9482288
Sum1959
Variance0.33806265
MonotonicityNot monotonic
2025-06-30T15:10:06.046772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 1191
52.6%
2 300
 
13.2%
3 39
 
1.7%
4 9
 
0.4%
5 1
 
< 0.1%
10 1
 
< 0.1%
(Missing) 725
32.0%
ValueCountFrequency (%)
1 1191
52.6%
2 300
 
13.2%
3 39
 
1.7%
4 9
 
0.4%
5 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
5 1
 
< 0.1%
4 9
 
0.4%
3 39
 
1.7%
2 300
 
13.2%
1 1191
52.6%

cant_autenticado
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)0.4%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.7742933
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.4 KiB
2025-06-30T15:10:06.109267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.82376969
Coefficient of variation (CV)0.46428045
Kurtosis19.383192
Mean1.7742933
Median Absolute Deviation (MAD)0
Skewness2.6606685
Sum4017
Variance0.6785965
MonotonicityNot monotonic
2025-06-30T15:10:06.172139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 1147
50.6%
1 867
38.3%
3 190
 
8.4%
4 37
 
1.6%
5 15
 
0.7%
6 4
 
0.2%
11 2
 
0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 2
 
0.1%
ValueCountFrequency (%)
1 867
38.3%
2 1147
50.6%
3 190
 
8.4%
4 37
 
1.6%
5 15
 
0.7%
6 4
 
0.2%
8 1
 
< 0.1%
9 1
 
< 0.1%
11 2
 
0.1%
ValueCountFrequency (%)
11 2
 
0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
6 4
 
0.2%
5 15
 
0.7%
4 37
 
1.6%
3 190
 
8.4%
2 1147
50.6%
1 867
38.3%

cant_noAutenticado
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)1.1%
Missing1222
Missing (%)53.9%
Infinite0
Infinite (%)0.0%
Mean1.3659004
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.4 KiB
2025-06-30T15:10:06.260648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.95749857
Coefficient of variation (CV)0.70100175
Kurtosis32.010006
Mean1.3659004
Median Absolute Deviation (MAD)0
Skewness4.7024076
Sum1426
Variance0.9168035
MonotonicityNot monotonic
2025-06-30T15:10:06.357659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 821
36.2%
2 142
 
6.3%
3 53
 
2.3%
6 8
 
0.4%
5 8
 
0.4%
4 7
 
0.3%
10 1
 
< 0.1%
9 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 1222
53.9%
ValueCountFrequency (%)
1 821
36.2%
2 142
 
6.3%
3 53
 
2.3%
4 7
 
0.3%
5 8
 
0.4%
6 8
 
0.4%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 8
 
0.4%
5 8
 
0.4%
4 7
 
0.3%
3 53
 
2.3%
2 142
6.3%

cant_sinMontoLimite
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3993822
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.4 KiB
2025-06-30T15:10:06.435865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile4
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.95403387
Coefficient of variation (CV)0.39761647
Kurtosis32.495831
Mean2.3993822
Median Absolute Deviation (MAD)0
Skewness4.5925269
Sum5437
Variance0.91018063
MonotonicityNot monotonic
2025-06-30T15:10:06.498359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 1708
75.4%
3 370
 
16.3%
4 116
 
5.1%
5 37
 
1.6%
6 15
 
0.7%
7 7
 
0.3%
8 4
 
0.2%
12 3
 
0.1%
11 2
 
0.1%
9 1
 
< 0.1%
Other values (3) 3
 
0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1708
75.4%
3 370
 
16.3%
4 116
 
5.1%
5 37
 
1.6%
6 15
 
0.7%
7 7
 
0.3%
8 4
 
0.2%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
12 3
 
0.1%
11 2
 
0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 4
 
0.2%
7 7
 
0.3%
6 15
 
0.7%
5 37
 
1.6%
4 116
5.1%

cant_MontoLimite
Categorical

High correlation  Missing 

Distinct2
Distinct (%)40.0%
Missing2261
Missing (%)99.8%
Memory size141.6 KiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)20.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4
 
0.2%
2.0 1
 
< 0.1%
(Missing) 2261
99.8%

Length

2025-06-30T15:10:06.592098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:06.639824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4
80.0%
2.0 1
 
20.0%

Most occurring characters

ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
1 4
26.7%
2 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
1 4
26.7%
2 1
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
1 4
26.7%
2 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
1 4
26.7%
2 1
 
6.7%

total_articulos_provee
Real number (ℝ)

High correlation 

Distinct240
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.909091
Minimum1
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.4 KiB
2025-06-30T15:10:06.702315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q331
95-th percentile163.75
Maximum850
Range849
Interquartile range (IQR)29

Descriptive statistics

Standard deviation73.974935
Coefficient of variation (CV)2.1190737
Kurtosis30.9183
Mean34.909091
Median Absolute Deviation (MAD)7
Skewness4.7236738
Sum79104
Variance5472.2911
MonotonicityNot monotonic
2025-06-30T15:10:06.813076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 426
18.8%
2 184
 
8.1%
4 129
 
5.7%
3 123
 
5.4%
5 82
 
3.6%
7 75
 
3.3%
6 68
 
3.0%
9 54
 
2.4%
8 52
 
2.3%
11 48
 
2.1%
Other values (230) 1025
45.2%
ValueCountFrequency (%)
1 426
18.8%
2 184
8.1%
3 123
 
5.4%
4 129
 
5.7%
5 82
 
3.6%
6 68
 
3.0%
7 75
 
3.3%
8 52
 
2.3%
9 54
 
2.4%
10 38
 
1.7%
ValueCountFrequency (%)
850 1
< 0.1%
780 1
< 0.1%
678 1
< 0.1%
677 1
< 0.1%
636 1
< 0.1%
621 1
< 0.1%
620 1
< 0.1%
611 1
< 0.1%
600 1
< 0.1%
581 1
< 0.1%

dmonto_total_adjudicado
Categorical

High correlation 

Distinct21
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size192.4 KiB
(89.439.449- 222.964.579]
171 
(222.964.579- 46.172.150.151]
171 
(46.718.747- 89.439.449]
156 
(30.451.916- 46.718.747]
150 
(13.557.176- 19.975.532]
 
141
Other values (16)
1477 

Length

Max length29
Median length25
Mean length21.9391
Min length3

Characters and Unicode

Total characters49714
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(89.439.449- 222.964.579]
2nd row(89.439.449- 222.964.579]
3rd row(13.557.176- 19.975.532]
4th row(2.483.085- 3.396.600]
5th row(30.451.916- 46.718.747]

Common Values

ValueCountFrequency (%)
(89.439.449- 222.964.579] 171
 
7.5%
(222.964.579- 46.172.150.151] 171
 
7.5%
(46.718.747- 89.439.449] 156
 
6.9%
(30.451.916- 46.718.747] 150
 
6.6%
(13.557.176- 19.975.532] 141
 
6.2%
(9.424.898- 13.557.176] 136
 
6.0%
(6.702.697- 9.424.898] 123
 
5.4%
(19.975.532- 30.451.916] 122
 
5.4%
(4.727.330- 6.702.697] 112
 
4.9%
(3.396.600- 4.727.330] 104
 
4.6%
Other values (11) 880
38.8%

Length

2025-06-30T15:10:06.934679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
222.964.579 342
 
7.6%
89.439.449 327
 
7.2%
46.718.747 306
 
6.8%
13.557.176 277
 
6.1%
30.451.916 272
 
6.0%
19.975.532 263
 
5.8%
9.424.898 259
 
5.7%
6.702.697 235
 
5.2%
4.727.330 216
 
4.8%
3.396.600 203
 
4.5%
Other values (12) 1816
40.2%

Most occurring characters

ValueCountFrequency (%)
. 7992
16.1%
7 4627
9.3%
9 4409
 
8.9%
4 3624
 
7.3%
3 3365
 
6.8%
1 3208
 
6.5%
2 3047
 
6.1%
6 2910
 
5.9%
5 2738
 
5.5%
0 2592
 
5.2%
Other values (7) 11202
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49714
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 7992
16.1%
7 4627
9.3%
9 4409
 
8.9%
4 3624
 
7.3%
3 3365
 
6.8%
1 3208
 
6.5%
2 3047
 
6.1%
6 2910
 
5.9%
5 2738
 
5.5%
0 2592
 
5.2%
Other values (7) 11202
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49714
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 7992
16.1%
7 4627
9.3%
9 4409
 
8.9%
4 3624
 
7.3%
3 3365
 
6.8%
1 3208
 
6.5%
2 3047
 
6.1%
6 2910
 
5.9%
5 2738
 
5.5%
0 2592
 
5.2%
Other values (7) 11202
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49714
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 7992
16.1%
7 4627
9.3%
9 4409
 
8.9%
4 3624
 
7.3%
3 3365
 
6.8%
1 3208
 
6.5%
2 3047
 
6.1%
6 2910
 
5.9%
5 2738
 
5.5%
0 2592
 
5.2%
Other values (7) 11202
22.5%

dcant_procesos_adjudicado
Categorical

High correlation 

Distinct10
Distinct (%)0.4%
Missing16
Missing (%)0.7%
Memory size168.9 KiB
(0.999, 2.0]
1059 
(2.0, 3.0]
231 
(8.0, 12.0]
161 
(3.0, 4.0]
148 
(19.0, 39.0]
133 
Other values (5)
518 

Length

Max length14
Median length12
Mean length11.423556
Min length10

Characters and Unicode

Total characters25703
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(39.0, 1214.0]
2nd row(12.0, 19.0]
3rd row(0.999, 2.0]
4th row(3.0, 4.0]
5th row(5.0, 6.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 1059
46.7%
(2.0, 3.0] 231
 
10.2%
(8.0, 12.0] 161
 
7.1%
(3.0, 4.0] 148
 
6.5%
(19.0, 39.0] 133
 
5.9%
(12.0, 19.0] 127
 
5.6%
(6.0, 8.0] 106
 
4.7%
(39.0, 1214.0] 101
 
4.5%
(5.0, 6.0] 93
 
4.1%
(4.0, 5.0] 91
 
4.0%
(Missing) 16
 
0.7%

Length

2025-06-30T15:10:07.048139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:07.145128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1290
28.7%
0.999 1059
23.5%
3.0 379
 
8.4%
12.0 288
 
6.4%
8.0 267
 
5.9%
19.0 260
 
5.8%
4.0 239
 
5.3%
39.0 234
 
5.2%
6.0 199
 
4.4%
5.0 184
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 4500
17.5%
. 4500
17.5%
9 3671
14.3%
( 2250
8.8%
, 2250
8.8%
2250
8.8%
] 2250
8.8%
2 1679
 
6.5%
1 750
 
2.9%
3 613
 
2.4%
Other values (4) 990
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25703
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4500
17.5%
. 4500
17.5%
9 3671
14.3%
( 2250
8.8%
, 2250
8.8%
2250
8.8%
] 2250
8.8%
2 1679
 
6.5%
1 750
 
2.9%
3 613
 
2.4%
Other values (4) 990
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25703
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4500
17.5%
. 4500
17.5%
9 3671
14.3%
( 2250
8.8%
, 2250
8.8%
2250
8.8%
] 2250
8.8%
2 1679
 
6.5%
1 750
 
2.9%
3 613
 
2.4%
Other values (4) 990
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25703
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4500
17.5%
. 4500
17.5%
9 3671
14.3%
( 2250
8.8%
, 2250
8.8%
2250
8.8%
] 2250
8.8%
2 1679
 
6.5%
1 750
 
2.9%
3 613
 
2.4%
Other values (4) 990
 
3.9%

dtotal_articulos_provee
Categorical

High correlation 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size169.6 KiB
(0.999, 2.0]
610 
(4.0, 6.0]
150 
(11.0, 15.0]
142 
(8.0, 11.0]
140 
(15.0, 21.0]
137 
Other values (10)
1087 

Length

Max length15
Median length12
Mean length11.63504
Min length10

Characters and Unicode

Total characters26365
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(97.6, 161.0]
2nd row(0.999, 2.0]
3rd row(0.999, 2.0]
4th row(58.0, 97.6]
5th row(0.999, 2.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 610
26.9%
(4.0, 6.0] 150
 
6.6%
(11.0, 15.0] 142
 
6.3%
(8.0, 11.0] 140
 
6.2%
(15.0, 21.0] 137
 
6.0%
(29.0, 40.0] 131
 
5.8%
(3.0, 4.0] 129
 
5.7%
(6.0, 8.0] 127
 
5.6%
(21.0, 29.0] 124
 
5.5%
(2.0, 3.0] 123
 
5.4%
Other values (5) 453
20.0%

Length

2025-06-30T15:10:07.260110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.0 733
16.2%
0.999 610
13.5%
11.0 282
 
6.2%
4.0 279
 
6.2%
15.0 279
 
6.2%
6.0 277
 
6.1%
8.0 267
 
5.9%
21.0 261
 
5.8%
29.0 255
 
5.6%
3.0 252
 
5.6%
Other values (6) 1037
22.9%

Most occurring characters

ValueCountFrequency (%)
. 4532
17.2%
0 4525
17.2%
9 2366
9.0%
( 2266
8.6%
, 2266
8.6%
2266
8.6%
] 2266
8.6%
1 1524
 
5.8%
2 1249
 
4.7%
6 746
 
2.8%
Other values (5) 2359
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26365
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 4532
17.2%
0 4525
17.2%
9 2366
9.0%
( 2266
8.6%
, 2266
8.6%
2266
8.6%
] 2266
8.6%
1 1524
 
5.8%
2 1249
 
4.7%
6 746
 
2.8%
Other values (5) 2359
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26365
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 4532
17.2%
0 4525
17.2%
9 2366
9.0%
( 2266
8.6%
, 2266
8.6%
2266
8.6%
] 2266
8.6%
1 1524
 
5.8%
2 1249
 
4.7%
6 746
 
2.8%
Other values (5) 2359
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26365
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 4532
17.2%
0 4525
17.2%
9 2366
9.0%
( 2266
8.6%
, 2266
8.6%
2266
8.6%
] 2266
8.6%
1 1524
 
5.8%
2 1249
 
4.7%
6 746
 
2.8%
Other values (5) 2359
8.9%

Cluster_6
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size146.1 KiB
3
2266 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2266
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 2266
100.0%

Length

2025-06-30T15:10:07.344495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:07.392338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 2266
100.0%

Most occurring characters

ValueCountFrequency (%)
3 2266
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2266
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 2266
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2266
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 2266
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2266
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 2266
100.0%

Interactions

2025-06-30T15:10:00.795608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:50.326368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:51.546226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:52.938745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:53.887035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:54.827161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:55.827537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:56.761179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:57.893277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:58.808742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:59.774892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:00.878175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:50.434070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:51.654740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:53.028669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:53.958735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:54.925281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:55.907482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:56.843896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:57.976389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:58.907778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:59.861831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:00.975170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:50.534795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:51.751721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:53.129682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:54.062098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:55.029141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:55.991881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:56.941896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:58.076707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:58.995753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:59.959924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:01.043249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:50.631514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:51.851387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:53.212260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:54.144675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:55.127498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:56.095521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:57.028148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:58.160922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:59.078771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:00.044394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:01.146578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:50.758816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:51.970131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:53.295016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:54.240946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:55.225622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:56.178706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:57.112578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:58.246227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:59.158873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:00.162184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:01.225514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:50.869288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:52.094491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:53.396187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-30T15:09:50.962023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:52.194028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:53.475553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:54.392105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:55.396605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:56.344964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:57.462719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:58.428471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:59.343071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:00.344072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:01.392256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:51.059635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:52.295463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:53.561310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:54.477386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:55.483194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:56.425434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:57.547190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:58.493219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:59.427376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:00.427105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:01.491010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:51.178676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:52.385708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:53.626576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:54.560641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:55.558797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:56.493128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:57.627466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:58.575489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:59.510116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:00.527605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:01.579700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:51.312949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:52.486367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:53.716294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:54.662058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:55.644183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:56.591025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:57.727584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:58.662143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:59.594035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:00.611105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:01.658988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:51.445750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:52.588660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:53.792211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:54.752487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:55.744337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:56.675498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:57.808776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:58.741157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:09:59.675622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:00.694592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-30T15:10:07.454829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EstadoTipoSocietarioanio_preinscripcionantiguedadcant_Apoderadocant_MontoLimitecant_antecedentescant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_sociosdcant_procesos_adjudicadodmonto_total_adjudicadodtotal_articulos_proveemonto_total_adjudicadoperiodo_preinscripcionprovinciatotal_articulos_provee
Estado1.0000.2280.1260.1370.0000.0001.0000.0570.0510.0000.0300.0080.0000.1030.1600.0690.0000.1180.2070.000
TipoSocietario0.2281.0000.1400.1500.0621.0001.0000.0700.0340.0000.1170.0730.1100.0440.1810.0610.0470.1300.3410.000
anio_preinscripcion0.1260.1401.0001.0000.0000.0001.0000.0000.0450.0900.0550.0150.0320.1630.1140.0720.0101.0000.1400.025
antiguedad0.1370.1501.0001.0000.0650.0001.000-0.0940.1150.389-0.0010.0990.0690.1800.1220.0720.233-0.9580.1380.151
cant_Apoderado0.0000.0620.0000.0651.0000.0001.0000.2460.5300.0890.0430.5690.1720.0720.0310.0000.091-0.0870.0000.031
cant_MontoLimite0.0001.0000.0000.0000.0001.0000.0000.0000.8160.0000.7070.0000.5770.0000.5770.0001.0000.0000.0001.000
cant_antecedentes1.0001.0001.0001.0001.0000.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.000
cant_autenticado0.0570.0700.000-0.0940.2460.0001.0001.0000.089-0.0280.1480.3030.0270.0560.0000.000-0.0040.1080.000-0.024
cant_noAutenticado0.0510.0340.0450.1150.5300.8161.0000.0891.0000.0910.2930.7660.2250.0350.0000.0360.125-0.1270.0000.031
cant_procesos_adjudicado0.0000.0000.0900.3890.0890.0001.000-0.0280.0911.000-0.0290.1050.0740.4600.1220.0870.583-0.4170.0000.276
cant_representante0.0300.1170.055-0.0010.0430.707NaN0.1480.293-0.0291.0000.2380.1400.0360.0000.020-0.0280.0020.0000.002
cant_sinMontoLimite0.0080.0730.0150.0990.5690.0001.0000.3030.7660.1050.2381.0000.1950.0660.0330.0230.144-0.1020.0000.023
cant_socios0.0000.1100.0320.0690.1720.5771.0000.0270.2250.0740.1400.1951.0000.0000.0310.0000.113-0.0800.0000.028
dcant_procesos_adjudicado0.1030.0440.1630.1800.0720.0001.0000.0560.0350.4600.0360.0660.0001.0000.1990.1030.1060.1500.0550.078
dmonto_total_adjudicado0.1600.1810.1140.1220.0310.5771.0000.0000.0000.1220.0000.0330.0310.1991.0000.0190.2230.1070.1280.022
dtotal_articulos_provee0.0690.0610.0720.0720.0000.0001.0000.0000.0360.0870.0200.0230.0000.1030.0191.0000.0270.0640.0340.605
monto_total_adjudicado0.0000.0470.0100.2330.0911.0001.000-0.0040.1250.583-0.0280.1440.1130.1060.2230.0271.000-0.2650.0000.066
periodo_preinscripcion0.1180.1301.000-0.958-0.0870.0001.0000.108-0.127-0.4170.002-0.102-0.0800.1500.1070.064-0.2651.0000.133-0.156
provincia0.2070.3410.1400.1380.0000.0001.0000.0000.0000.0000.0000.0000.0000.0550.1280.0340.0000.1331.0000.056
total_articulos_provee0.0000.0000.0250.1510.0311.0001.000-0.0240.0310.2760.0020.0230.0280.0780.0220.6050.066-0.1560.0561.000

Missing values

2025-06-30T15:10:01.827701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-30T15:10:02.035233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-30T15:10:02.240471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveeCluster_6
820305924076Suministros EDA13/10/2016InscriptoPersona Física2016102016147.09.393091e+075.0CABANaNNaNNaNNaN2.0NaN1.01.02.0NaN107.0(89.439.449- 222.964.579](39.0, 1214.0](97.6, 161.0]3
1430710210000TECNARAN SRL06/09/2016InscriptoS.R.L201609201619.01.289448e+085.0CABA1.0NaNNaNNaN1.01.02.0NaN2.0NaN1.0(89.439.449- 222.964.579](12.0, 19.0](0.999, 2.0]3
1533714924619SIGNIFY ARGENTINA S.A.18/10/2016InscriptoSociedad Anónima20161020161.01.943875e+075.0Buenos Aires4.0NaNNaNNaN2.01.01.02.03.0NaN1.0(13.557.176- 19.975.532](0.999, 2.0](0.999, 2.0]3
1733561600959Rognoni y CIA SA21/09/2016InscriptoSociedad Anónima20160920164.02.651773e+065.0Buenos Aires2.0NaNNaNNaNNaN2.02.0NaN2.0NaN71.0(2.483.085- 3.396.600](3.0, 4.0](58.0, 97.6]3
1930694465591ADSUR S.A..22/09/2016InscriptoSociedad Anónima20160920166.04.605003e+075.0CABA2.0NaNNaNNaN1.01.01.01.02.0NaN1.0(30.451.916- 46.718.747](5.0, 6.0](0.999, 2.0]3
2630707870571BASESIDE S.R.L.19/08/2016InscriptoS.R.L201608201620.08.117709e+065.0CABA2.0NaNNaNNaN1.01.01.01.02.0NaN6.0(6.702.697- 9.424.898](19.0, 39.0](4.0, 6.0]3
2830708995157Netlabs SRL24/08/2016Desactualizado doc. vencidosS.R.L20160820163.03.583917e+065.0CABA2.0NaNNaNNaN1.01.01.01.02.0NaN237.0(3.396.600- 4.727.330](2.0, 3.0](161.0, 345.0]3
3430500106316LA LEY SOCIEDAD ANONIMA, EDITORA E IMPRESORA21/07/2016InscriptoSociedad Anónima2016072016189.02.752135e+085.0CABA3.0NaNNaNNaN11.0NaN1.010.011.0NaN3.0(222.964.579- 46.172.150.151](39.0, 1214.0](2.0, 3.0]3
3630680075995MOST S.A05/09/2016InscriptoSociedad Anónima20160920165.08.024817e+065.0CABA1.0NaNNaNNaN1.01.01.01.02.0NaN16.0(6.702.697- 9.424.898](4.0, 5.0](15.0, 21.0]3
3830707787070Ediciones Rap s.a.16/08/2016InscriptoSociedad Anónima201608201645.04.983273e+065.0CABA2.0NaNNaNNaN2.0NaN2.0NaN2.0NaN4.0(4.727.330- 6.702.697](39.0, 1214.0](3.0, 4.0]3
CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveeCluster_6
1002633541350999SOCIEDAD RURAL DE JESUS MARIA17/11/2021InscriptoOtras Formas Societarias20211120211.02.898882e+060.0Córdoba3.0NaNNaNNaN1.02.03.0NaN3.0NaN1.0(2.483.085- 3.396.600](0.999, 2.0](0.999, 2.0]3
1003030642796131PERFIL S.R.L.29/07/2020InscriptoS.R.L20200720201.01.037790e+061.0Neuquén7.0NaNNaNNaN1.01.02.0NaN2.0NaN25.0(890.758- 1.302.657](0.999, 2.0](21.0, 29.0]3
1003830527725247SODA DI MARCO S.R.L.16/10/2018InscriptoS.R.L20181020181.06.557143e+043.0Mendoza3.0NaNNaNNaN2.0NaN2.0NaN2.0NaN3.0(33.011- 104.767](0.999, 2.0](2.0, 3.0]3
1004030674562620INAR VIAL S.A.22/08/2022InscriptoSociedad Anónima20220820222.07.810812e+060.0Santa Fe5.0NaNNaNNaN1.04.01.04.05.0NaN1.0(6.702.697- 9.424.898](0.999, 2.0](0.999, 2.0]3
1004120086498295sin datos20/12/2021InscriptoPersona Física20211220211.06.838163e+050.0La RiojaNaNNaNNaNNaN2.0NaN2.0NaN2.0NaN5.0(599.760- 890.758](0.999, 2.0](4.0, 6.0]3
1004330713790407PEDRO GOITIA S.R.L.20/03/2017InscriptoS.R.L20170320171.01.997880e+074.0CABA3.0NaNNaNNaN1.01.02.0NaN2.0NaN9.0(19.975.532- 30.451.916](0.999, 2.0](8.0, 11.0]3
1005130707885595Dirsin Corporation S.A.23/02/2018Desactualizado mantenciónSociedad Anónima20180220181.03.166163e+073.0CABA1.0NaNNaNNaN1.01.02.0NaN2.0NaN1.0(30.451.916- 46.718.747](0.999, 2.0](0.999, 2.0]3
1005720414755632sin datos13/09/2022InscriptoPersona Física20220920221.02.145238e+060.0ChubutNaNNaNNaNNaN2.0NaN1.01.02.0NaN1.0(1.793.326- 2.483.085](0.999, 2.0](0.999, 2.0]3
1006220149549987TRANSPORTES ROBOL07/02/2017Desactualizado mantenciónPersona Física20170220171.05.261905e+064.0CorrientesNaNNaNNaNNaN2.0NaN2.0NaN2.0NaN4.0(4.727.330- 6.702.697](0.999, 2.0](3.0, 4.0]3
1007230518773743Hotel Astor Sociedad Anonima Comercial25/07/2022InscriptoSociedad Anónima20220720221.01.757476e+070.0CABA2.0NaNNaNNaN1.01.01.01.02.0NaN6.0(13.557.176- 19.975.532](0.999, 2.0](4.0, 6.0]3